{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.7 Lab 3: PCR and PLS Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:33.189208Z",
     "start_time": "2023-10-07T12:10:33.009426Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.cross_decomposition import PLSRegression\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:33.206962Z",
     "start_time": "2023-10-07T12:10:33.189720Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(322, 21)\n"
     ]
    },
    {
     "data": {
      "text/plain": "          Unnamed: 0  AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  \\\n0     -Andy Allanson    293    66      1    30   29     14      1     293   \n1        -Alan Ashby    315    81      7    24   38     39     14    3449   \n2       -Alvin Davis    479   130     18    66   72     76      3    1624   \n3      -Andre Dawson    496   141     20    65   78     37     11    5628   \n4  -Andres Galarraga    321    87     10    39   42     30      2     396   \n\n   CHits  ...  CRuns  CRBI  CWalks  League Division PutOuts  Assists  Errors  \\\n0     66  ...     30    29      14       A        E     446       33      20   \n1    835  ...    321   414     375       N        W     632       43      10   \n2    457  ...    224   266     263       A        W     880       82      14   \n3   1575  ...    828   838     354       N        E     200       11       3   \n4    101  ...     48    46      33       N        E     805       40       4   \n\n   Salary  NewLeague  \n0     NaN          A  \n1   475.0          N  \n2   480.0          A  \n3   500.0          N  \n4    91.5          N  \n\n[5 rows x 21 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>AtBat</th>\n      <th>Hits</th>\n      <th>HmRun</th>\n      <th>Runs</th>\n      <th>RBI</th>\n      <th>Walks</th>\n      <th>Years</th>\n      <th>CAtBat</th>\n      <th>CHits</th>\n      <th>...</th>\n      <th>CRuns</th>\n      <th>CRBI</th>\n      <th>CWalks</th>\n      <th>League</th>\n      <th>Division</th>\n      <th>PutOuts</th>\n      <th>Assists</th>\n      <th>Errors</th>\n      <th>Salary</th>\n      <th>NewLeague</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-Andy Allanson</td>\n      <td>293</td>\n      <td>66</td>\n      <td>1</td>\n      <td>30</td>\n      <td>29</td>\n      <td>14</td>\n      <td>1</td>\n      <td>293</td>\n      <td>66</td>\n      <td>...</td>\n      <td>30</td>\n      <td>29</td>\n      <td>14</td>\n      <td>A</td>\n      <td>E</td>\n      <td>446</td>\n      <td>33</td>\n      <td>20</td>\n      <td>NaN</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-Alan Ashby</td>\n      <td>315</td>\n      <td>81</td>\n      <td>7</td>\n      <td>24</td>\n      <td>38</td>\n      <td>39</td>\n      <td>14</td>\n      <td>3449</td>\n      <td>835</td>\n      <td>...</td>\n      <td>321</td>\n      <td>414</td>\n      <td>375</td>\n      <td>N</td>\n      <td>W</td>\n      <td>632</td>\n      <td>43</td>\n      <td>10</td>\n      <td>475.0</td>\n      <td>N</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-Alvin Davis</td>\n      <td>479</td>\n      <td>130</td>\n      <td>18</td>\n      <td>66</td>\n      <td>72</td>\n      <td>76</td>\n      <td>3</td>\n      <td>1624</td>\n      <td>457</td>\n      <td>...</td>\n      <td>224</td>\n      <td>266</td>\n      <td>263</td>\n      <td>A</td>\n      <td>W</td>\n      <td>880</td>\n      <td>82</td>\n      <td>14</td>\n      <td>480.0</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-Andre Dawson</td>\n      <td>496</td>\n      <td>141</td>\n      <td>20</td>\n      <td>65</td>\n      <td>78</td>\n      <td>37</td>\n      <td>11</td>\n      <td>5628</td>\n      <td>1575</td>\n      <td>...</td>\n      <td>828</td>\n      <td>838</td>\n      <td>354</td>\n      <td>N</td>\n      <td>E</td>\n      <td>200</td>\n      <td>11</td>\n      <td>3</td>\n      <td>500.0</td>\n      <td>N</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-Andres Galarraga</td>\n      <td>321</td>\n      <td>87</td>\n      <td>10</td>\n      <td>39</td>\n      <td>42</td>\n      <td>30</td>\n      <td>2</td>\n      <td>396</td>\n      <td>101</td>\n      <td>...</td>\n      <td>48</td>\n      <td>46</td>\n      <td>33</td>\n      <td>N</td>\n      <td>E</td>\n      <td>805</td>\n      <td>40</td>\n      <td>4</td>\n      <td>91.5</td>\n      <td>N</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'../data/Hitters.csv')\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:33.227030Z",
     "start_time": "2023-10-07T12:10:33.205207Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape is  (322, 23)\n"
     ]
    },
    {
     "data": {
      "text/plain": "   AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  ...  \\\n0    293    66      1    30   29     14      1     293     66       1  ...   \n1    315    81      7    24   38     39     14    3449    835      69  ...   \n2    479   130     18    66   72     76      3    1624    457      63  ...   \n3    496   141     20    65   78     37     11    5628   1575     225  ...   \n4    321    87     10    39   42     30      2     396    101      12  ...   \n\n   PutOuts  Assists  Errors  Salary  League_A  League_N  Division_E  \\\n0      446       33      20     NaN      True     False        True   \n1      632       43      10   475.0     False      True       False   \n2      880       82      14   480.0      True     False       False   \n3      200       11       3   500.0     False      True        True   \n4      805       40       4    91.5     False      True        True   \n\n   Division_W  NewLeague_A  NewLeague_N  \n0       False         True        False  \n1        True        False         True  \n2        True         True        False  \n3       False        False         True  \n4       False        False         True  \n\n[5 rows x 23 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>AtBat</th>\n      <th>Hits</th>\n      <th>HmRun</th>\n      <th>Runs</th>\n      <th>RBI</th>\n      <th>Walks</th>\n      <th>Years</th>\n      <th>CAtBat</th>\n      <th>CHits</th>\n      <th>CHmRun</th>\n      <th>...</th>\n      <th>PutOuts</th>\n      <th>Assists</th>\n      <th>Errors</th>\n      <th>Salary</th>\n      <th>League_A</th>\n      <th>League_N</th>\n      <th>Division_E</th>\n      <th>Division_W</th>\n      <th>NewLeague_A</th>\n      <th>NewLeague_N</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>293</td>\n      <td>66</td>\n      <td>1</td>\n      <td>30</td>\n      <td>29</td>\n      <td>14</td>\n      <td>1</td>\n      <td>293</td>\n      <td>66</td>\n      <td>1</td>\n      <td>...</td>\n      <td>446</td>\n      <td>33</td>\n      <td>20</td>\n      <td>NaN</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>315</td>\n      <td>81</td>\n      <td>7</td>\n      <td>24</td>\n      <td>38</td>\n      <td>39</td>\n      <td>14</td>\n      <td>3449</td>\n      <td>835</td>\n      <td>69</td>\n      <td>...</td>\n      <td>632</td>\n      <td>43</td>\n      <td>10</td>\n      <td>475.0</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>479</td>\n      <td>130</td>\n      <td>18</td>\n      <td>66</td>\n      <td>72</td>\n      <td>76</td>\n      <td>3</td>\n      <td>1624</td>\n      <td>457</td>\n      <td>63</td>\n      <td>...</td>\n      <td>880</td>\n      <td>82</td>\n      <td>14</td>\n      <td>480.0</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>496</td>\n      <td>141</td>\n      <td>20</td>\n      <td>65</td>\n      <td>78</td>\n      <td>37</td>\n      <td>11</td>\n      <td>5628</td>\n      <td>1575</td>\n      <td>225</td>\n      <td>...</td>\n      <td>200</td>\n      <td>11</td>\n      <td>3</td>\n      <td>500.0</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>321</td>\n      <td>87</td>\n      <td>10</td>\n      <td>39</td>\n      <td>42</td>\n      <td>30</td>\n      <td>2</td>\n      <td>396</td>\n      <td>101</td>\n      <td>12</td>\n      <td>...</td>\n      <td>805</td>\n      <td>40</td>\n      <td>4</td>\n      <td>91.5</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 23 columns</p>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop('Unnamed: 0',axis = 1,inplace = True)\n",
    "qual_variables = [col for col in data.columns if data[col].dtype == 'O']\n",
    "data = pd.get_dummies(data,columns = qual_variables)\n",
    "print('Shape is ',data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:33.284134Z",
     "start_time": "2023-10-07T12:10:33.214639Z"
    }
   },
   "outputs": [],
   "source": [
    "# removing the null values\n",
    "data.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:33.298642Z",
     "start_time": "2023-10-07T12:10:33.217259Z"
    }
   },
   "outputs": [],
   "source": [
    "X = data.drop('Salary',axis = 1)\n",
    "y = data['Salary']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.7.1 Principal Components Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:33.300883Z",
     "start_time": "2023-10-07T12:10:33.222386Z"
    }
   },
   "outputs": [],
   "source": [
    "# we have to scale the predictors before applying pca\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "#applying pca\n",
    "pca = PCA()\n",
    "X_pca = pca.fit_transform(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:33.301355Z",
     "start_time": "2023-10-07T12:10:33.228452Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data returned by pca is  (263, 22)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of data returned by pca is ',X_pca.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:33.301793Z",
     "start_time": "2023-10-07T12:10:33.231996Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of components are -  22\n"
     ]
    }
   ],
   "source": [
    "print('Total number of components are - ',len(pca.components_))\n",
    "# we can see that there are total 22 principal axis. Each features axis is represented as a 22-dimensional vector\n",
    "# If our dataset was 2-dimensional, than each principal axis ( here 2), would have been a line (2-dimensional vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:33.302138Z",
     "start_time": "2023-10-07T12:10:33.237258Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([3.33519423e+01, 1.98329668e+01, 1.59398706e+01, 9.00942497e+00,\n       7.60093752e+00, 3.81576505e+00, 3.15504103e+00, 2.34828437e+00,\n       1.21007079e+00, 1.13336303e+00, 8.39744718e-01, 5.94024833e-01,\n       4.35033641e-01, 2.77562127e-01, 2.37062334e-01, 1.27338736e-01,\n       6.40680776e-02, 2.21021282e-02, 5.39704206e-03, 3.92798025e-31,\n       8.77974729e-32, 5.28655632e-32])"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets see the vriance explained by each of the above mentioned principal components\n",
    "pca.explained_variance_ratio_*100\n",
    "#This is not so clear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:33.387713Z",
     "start_time": "2023-10-07T12:10:33.242109Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([33.4, 19.8, 15.9,  9. ,  7.6,  3.8,  3.2,  2.3,  1.2,  1.1,  0.8,\n        0.6,  0.4,  0.3,  0.2,  0.1,  0.1,  0. ,  0. ,  0. ,  0. ,  0. ])"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# round it to 1 decimal points\n",
    "np.round(pca.explained_variance_ratio_*100,1)\n",
    "\n",
    "# we can see from the output of this cell that the first princiap component explains 33.4% of the variance, whereas the second\n",
    "# explains about 19.8% of the variance of the dataset. the last few principal components are irrevalent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCR - principal component regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:33.388098Z",
     "start_time": "2023-10-07T12:10:33.245356Z"
    }
   },
   "outputs": [],
   "source": [
    "# What we did aboce was actually PCA, in PCR, we use the data we got by PCA and fit a linear regression model to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:33.388179Z",
     "start_time": "2023-10-07T12:10:33.248329Z"
    }
   },
   "outputs": [],
   "source": [
    "# we will also select the value of num_component, which is a hyperparamter we feed to PCA, incdicating in how many components \n",
    "# we are going to reduce the dimension of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:33.404797Z",
     "start_time": "2023-10-07T12:10:33.253210Z"
    }
   },
   "outputs": [],
   "source": [
    "def pcr(X,y):\n",
    "    scaler = StandardScaler()\n",
    "    X_scaled = scaler.fit_transform(X)\n",
    "    \n",
    "    num_components = X.shape[1]\n",
    "    scores = []\n",
    "    variability_explained = []\n",
    "    \n",
    "    for n_component in range(1,num_components+1):\n",
    "        pca = PCA(n_components=n_component)\n",
    "        X_pca = pca.fit_transform(X_scaled)\n",
    "        \n",
    "        lr = LinearRegression()\n",
    "        scores.append(-np.mean(cross_val_score(lr,X_pca,y,cv = 10,scoring = 'neg_mean_squared_error')))\n",
    "        variability_explained.append(np.sum(np.round(pca.explained_variance_ratio_*100,1)))\n",
    "        \n",
    "    return scores,variability_explained    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:33.921174Z",
     "start_time": "2023-10-07T12:10:33.259149Z"
    }
   },
   "outputs": [],
   "source": [
    "scores,var_explained = pcr(X,y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:33.921401Z",
     "start_time": "2023-10-07T12:10:33.402704Z"
    }
   },
   "outputs": [],
   "source": [
    "num_components = np.arange(1,X.shape[1]+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:34.047532Z",
     "start_time": "2023-10-07T12:10:33.405623Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, '% explained by num of components')"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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nR8OHD7dbVvzCk+n/isDAQDVp0kSHDh2yPZ/mzZurYcOGmjt3rm2aVfv27bVt2za7hR9iY2PtVg0szoXFzmq1at26dWrZsqWtSFksFvXr108bNmzQhg0bdOedd/6l53Op53fq1Cm7bb/++mupPPafRUVFSZJWr15tt/2LL75QQUHBRVPpitOmTRv99ttvdgsqJCcn6+GHH9bGjRvVtm1bHT9+XFu3brX7uc8++0ze3t5q0aLF33gmhYobn9dee62qVKly0XONj4/Xb7/9dtmjRpcSFRWlzMxMGYZhNwYPHDig//znP8rPzy/xY/35yNGSJUsUHR2t3Nxc+fj4qEOHDpoyZYok2a3yBwCXwpEmAOXW66+/rltvvVUNGjSQVDh9p2HDhpo9e7a6d++u9evXa/r06Zf9+ZEjR+ruu+/W8OHDdc8998jX11fLly/Xhg0bbKuPxcTE6L333tNjjz2mevXq6dFHH9WXX36pCRMm6MMPP7Qte37q1Ck98sgj+uc//6mTJ0/qlVdeUadOndSuXbuL9tu0aVN5eXlp1qxZeuCBB5Sbm6uPPvrIdv7V3zl6MnbsWA0bNkxPPPGEbr/9dhUUFGjx4sXavn27baW3++67TytXrtSDDz6oRx99VAUFBXrttddsz+VKXnvtNRUUFKhGjRr63//+p8OHD+udd96xu0+/fv30xhtvyDAM9e3b9y8/nwt17dpVCxYs0Pz583X99dfr22+/veKy4X9VgwYNdOedd2ru3LnKzs5Wu3bttGfPHs2dO1ft2rXTjTfeWOLHGjp0qD755BM9+OCDGjFihHx9fbVgwQJVrVpVffv2la+vrz744AONHj1aY8aMUZ06dfT1119r1apVGj16tCpWrPi3n8+VxufYsWM1ceJEPf744+rbt69SUlI0d+5cVapU6ZJHYi+nc+fOtqXAR44cqfr162vHjh1644031KlTp0seeb2couf9+eefq2XLlmrfvr1efvlljRo1Sv/4xz/k6empZcuWycfHR127dnXsFwKg3KE0ASiX9u/fr3Xr1tmdj2SxWDRr1iyNHz9e//d//6d7771X3bt3v+xjNG7cWEuXLtWrr76qp556SoZhKCIiQv/5z3908803KzMzUxMnTlRERIQefPBBSYXnUk2ePFnDhg3Tm2++qTFjxkgqXCCgYsWKeuyxx1ShQgXdeeedevzxxy+53/DwcM2ePVtz587VI488okqVKun666/Xe++9pyFDhuiXX35Ro0aN/tLvpVOnTnr77bc1d+5cjRkzRt7e3mratKneeecd20IIISEh+t///qdp06ZpwoQJCggI0EMPPXTJc7suZdq0aXrppZd09OhRRUREaNGiRbYjM0WqVaumxo0bKyQkRDVq1PhLz+XPhg8fruTkZC1evFh5eXnq0qWLpk2bZiuDpW3atGkKDw/XqlWr9Pbbb6tq1aoaMmSIRo0a5dD5MzVq1NAHH3ygWbNmaeLEifLx8VFUVJRmzZplmxr33nvvafbs2ZozZ47S09NVr149TZs2zbbs9t91pfHZr18/BQQEaMGCBRo1apQCAwN14403auzYsSU+100qPDq0cOFCvf7661qwYIGSkpJUrVo1DR06VKNGjXIoc48ePfTpp59qwoQJGjBggJ5//nnNnz9f//nPfzR27FgVFBSoWbNmWrx4serVq+fQYwMofyzGX53HAQAoFdHR0YqKitLMmTPNjuIyEhISFB0drVdeeUU9e/Y0O065xvgEAI40AQBcyJ49e7Rx40Z9+eWXql27trp162Z2JAAAWAgCAOA6cnJy9M4779jOk7rwOjwAAJiF6XkAAAAAUAyONAEAAABAMShNAAAAAFAMShMAAAAAFIPSBAAAAADFoDQBAAAAQDHK7XWakpPTFBoapKSkNLF+IFyJxSKFhTE24ZoYn3BljE+4Msanayp6Xa6k3JamosFqGGLgwiUxNuHKGJ9wZYxPuDLGp3tieh4AAAAAFIPSBAAAAADFoDQBAAAAQDEoTQAAAABQDEoTAAAAABSD0gQAAAAAxaA0AQAAAEAxKE0AAAAAUAxKEwAAAAAUg9IEAAAAAMWgNAEAAABAMShNAAAAAFAMShMAAAAAFMP00pScnKzu3btr8+bNtm3bt2/XwIEDFRkZqejoaK1YscLuZz7++GN1795d119/vfr166dt27Zd7dgAAAAAyglTS9PWrVs1aNAgxcXF2balpqZq2LBh6tu3r2JiYjRt2jTNmDFDO3bskCRt3rxZU6ZM0cyZMxUTE6Pbb79djzzyiLKyssx6GgAAAADKMNNK08cff6wnn3xSjz/+uN32r776SsHBwRo8eLC8vLzUoUMH9enTR0uXLpUkrVixQrfddptat24tb29vDR06VCEhIVqzZo0ZTwMAAABAGedl1o47deqkPn36yMvLy644HThwQBEREXb3bdCggVauXClJio2NVf/+/S+6fe/evQ7t32Kx/xNwFYxNuDLGJ1wZ4xOujPHpmkr6ephWmqpUqXLJ7RkZGfL397fb5ufnp8zMzBLdXlKhoUGSpLCwIId+DrhaGJtwZYxPuLKrOT4Nw1BegaF8q7XwzwKrCqyG8qyFXxfdll9gKP8S2/L+dP/8AkN5f7p/vrXwfpfblmctfIyixyu6DXBVnh4WPdSpnjo1rGx2lBIzrTRdjr+/v9LS0uy2ZWdnKyAgwHZ7dnb2RbeHhIQ4tJ/k5DSFhgYpKSlNhvH3MgOlyWIp/AefsQlXxPjE1WQYhnLyrcrOtyo7r8D2dc757+2251mVnV8gHz8fnUvLthWPAuv5omG1Lxwl2n6+3Fx83z+2F/D3APhLKnha1DjE1+wYtn/XrsTlSlNERIR+/PFHu22xsbFq2LChJKlhw4Y6cODARbffdNNNDu2n6B97wxD/8MMlMTbhyhifMAxD2flWZeTkKz2nQBl5BXbF5pJfny82RV/n5BcVnwu/ti9H7sjDInl5WOTl4SEvT4s8LRZ5eVrObyvc7ln0dQm2exY9Vgm2e17wWBZL4QdC/q66BotFCgryU1padrl/Tbw8LGp/TYhb/R5crjR1795ds2bN0pIlSzR48GBt3bpVq1ev1rx58yRJAwYM0KhRo3TrrbeqdevWWrp0qZKSktS9e3eTkwMA4B7yCqxKz8lXRm5Bif/MyMlX+p/+vJpHWbw9LfL18pCfl6f8vD0u+bWvt4cqVvBVfl7+nwrEHwWmZNv/KCL2heeC7Zd4jKL7enDSCi7BYpEqVw5SYiJH6t2Ry5WmkJAQLV68WNOmTdOcOXMUGhqqSZMmqX379pKkDh06aPLkyXr++eeVkJCgBg0aaNGiRQoODjY3OAAAV4FhGMrILdDZrDylZuXpbHb++aM9JS9BuaXYdjwsUoCPlyr4eMrPy0N+3oV/+jr6tfflC5Gvl6e8PK5cRPhQCsBZLIZRPt9WkpLSFBbGGytcD//ow5UxPktXgdVQWna+zmafL0BZ+Uq9xNdF5Sg1K0+p2fkqsJbOL7+Ct6cCfD0V4OOpQF8vB/7842t/bw9ZXOTICuMTrozx6ZqKXpcrcbkjTQAAuKO8Aqt9ubng67Pny07R9qKvz2Xn669+dvLz8lAlf29V8vNSRb/CIhPo63mZPy8oPL6eCjx/ZMizBEdvAACUJgAAipVXYFVCWo7tv9MXfJ2YkWsrQRm5BX95H4G+nqrk561gf29V8vcqwdfe8vUy7fr0AFDuUJoAAOVWvtVQYnqOXSn683/JmXklfjwPi1TRr/DoTyX/8wXncl+fL0GV/Lzk5UkBAgBXRmkCAJRJVsNQckaurfycsh0pKtqWrcSMXJXk9CAfT4uqBfle9F/lQF8FX1CIgvy8WDkNAMogShMAwO0YhqHUrHy7MlRUhIqmz51Oz1V+CRqRp4dFVQN9LlmKiv4L9vd2mcUOAABXH6UJAOCy0nPydTQ5U0dTsnQ0JUtx57+OT8lSdgkufOphkSoH/FGIqp7/s/oF34dW8GFBBABAsShNAABT5RVYdfxsdmEpSsm0K0dXOp8otIJ3sUeIKgf4cL4QAOBvozQBAJzOMAwlZuQqLiXLduSo6OsTqdkq7lqrlQN8VDfEX+Gh/goPqaC6If6qG+KvGhX95MMKcgCAq4DSBAAoNZm5BYpLyTxfiLJ09PzXcSlZxS7J7e/tobohFRR+vhCFh/5RjgJ9+acKAGAu/iUCADgkv8Cq+D+VoqKjR2fScy/7cx4WqWYlv8JSdP6IUdHRoyqBPiy0AABwWZQmAMBl5RdYdTAxU7sS0rT7VJr2nErT4eRM5RUzny7Y31vh5wuR7ehRqL9qV/JnOh0AwC1RmgAAkgqvaxSXnKXd5wvS7lNp2n8mQzmXWKXOx9OiOuePGBWWoz+OHlXy9zYhPQAAzkNpAoByyDAMJaTlaPepNO06la7dCYVHkS513lGgr6euqxakJtWD1LR6kDo0ribfgnxZxHQ6AED5QGkCgHLgbGZe4RS7k2m2I0mXWs7b18tDEVUC1bRGkJpUD1STakGqE+Ivj/PnG1ksUuXQCkpMTJNx5evGAgBQJlCaAKCMycjN196EdNsUu92n0nTiXM5F9/O0SPUrB6hJ9SDbf/XDKnBdIwAA/oTSBABuLDffqgNn0m1T7HafTNOR5Exd6iBQeIi/XUGKqBIgP2/Pq54ZAAB3Q2kCADdRYDV0OCmz8OjR+Sl2B85kKN96cUWqFuRbWI6qBapJ9SBdVy1IQX685QMA8FfwLygAuKjUrDztPJmmHSdStePEOe06laasvItXsgv297adf1R0FCkswMeExAAAlE2UJgBwAVbD0NHkLFtB+v1E4fWQ/qyCt6canz961PR8QapR0ZcLwwIA4ESUJgAwQWZugXafStOOE+cKS9LJczqXnX/R/cJD/NW8ZkW1qFlRzWtW1LWhFeTpQUECAOBqojQBgJMZhqGT53L+KEgnzunAmXQV/OlUJF8vDzWtHqQWRSWpRkUFV+BCsQAAmI3SBAClLDffqn2n020laceJc0rMyL3oftWCfG0FqUXNioqoEsBy3wAAuCBKEwD8TUkZufr9goK0JyFNuX86jOTpYVHjqoG2aXbNawSpekU/kxIDAABHUJoAwAEFVkMHEzPsjiIdT82+6H7B/t52R5GuqxbINZEAAHBTlCYAKIZhGNpx4pw2HUnRjhPntPNkmjLzCuzuY5FUr3KFC0pSJdUJ9mNFOwAAyghKEwBcQk6+VV/tPa3l205o3+l0u9sCfDzVrMYfCzY0q1FRgb68nQIAUFbxrzwAXCAxPUcrt5/UR9tPKiUrT1LhqnZdGoQpsnYltahZUfXCAlj2GwCAcoTSBACSdp1K07Jfj2vDvjPKtxYu4lA10Ed3RdbSHc2rK9ifpb8BACivKE0Ayq38Aqu+PpCoZb+e0O8nz9m2t6hZUfe0qqUuDSvLiyNKAACUe5QmAOXO2aw8fbzjpFb+dkKn0wuvn+TlYVGPxlU0KLKWmlQPMjkhAABwJZQmAOVGbGKGlv16XOv2nFZOvlWSFFrBW/1b1lC/ljVVOcDH5IQAAMAVUZoAlGkFVkM/HErWsm3H9UvcWdv2RlUDdXermurRqKp8vDzMCwgAAFwepQlAmZSek6/Pdp7Sh9tO2C4+62GRujasrEGRtXR9rYpcRwkAAJQIpQlAmRKXkqUPtx3X6p0JtovQBvl6qW/z6hoYWVM1KvqZnBAAALgbShMAt2cYhrYcPatl247rx0PJMs5vvza0gu5uVVO3Nqkmf29PUzMCAAD3RWkC4Lay8wq0Zs9pLfv1uA4nZdq2d6oXqrsjaykqPJgpeAAA4G+jNAFwO6fOZWvFbyf0ye+ndC47X5JUwdtTfZpV012RtVQ3xN/khAAAoCyhNAFwC4ZhaPvxc1q27bi+PZCogvNz8GpV8tNdkTV1e7PqCvTlLQ0AAJQ+PmEAcGm5+VZt2H9Gy349rj0J6bbtbeoG6+7IWupUL1SeHkzBAwAAzkNpAuCSCqyGPtpxUm/9fFTJmXmSJF8vD91yXVXdHVlLDaoEmJwQAACUF5QmAC7n9xPn9OLGWO07XXhkqWqgjwZcX1N3Nq+h4AreJqcDAADlDaUJgMs4m5mnuT8c1qe/n5JUeH2lER2vUb8W1eXl6WFyOgAAUF5RmgCYrsBq6NOdpzTv+8NKPb8aXu+m1fToTdcqtIKPyekAAEB5R2kCYKrdp9L04sZY7T6VJklqWCVA429uoJa1KpmcDAAAoBClCYApUrPy9OaPR/TR9pMyJAX4eGpEx2s04Pqa8mI1PAAA4EIoTQCuKqth6POdCXrj+8M6m1W4Kt6t11XVmJuuVeVAX5PTAQAAXIzSBOCq2ZeQrhc3xur3k+ckSfXCKuipmxuodZ1gc4MBAAAUg9IEwOnSsvM1/8cjWrn9hKyGVMHbUw/fEK67I2uyKh4AAHB5lCYATmMYhtbsPq05/3fIdoHa7o2q6LHO9VQ1iKl4AADAPVCaADhF7JkMvbjxgH47XjgV75pQf42LbqCo8BCTkwEAADiG0gSgVKXn5GvhT0f14bbjKjAkPy8PPdQhXPe2riVvpuIBAAA3RGkCUCoMw9CXe8/ote8OKSkjV5IU3bCyHu9ST9Ur+pmcDgAA4K+jNAH42w4lZWjWxlj9Ep8qSaob4q8no+urwzWhJicDAAD4+1xyrszBgwf14IMPqk2bNurSpYvefPNNWa1WSdL27ds1cOBARUZGKjo6WitWrDA5LVB+ZeYWaM53h3Tvu7/ql/hU+Xp56JGO1+h//2xNYQIAAGWGyx1pysjI0EMPPaSOHTvqjTfeUEpKikaMGKGCggINGTJEw4YN05gxYzRo0CDFxMRo1KhRatSokVq0aGF2dKDcMAxDG/cn6tVvD+p0euFUvM71wzS2a33VrMRUPAAAULa4XGnaunWrkpKS9Nxzz8nHx0cVKlTQI488omnTpqlq1aoKDg7W4MGDJUkdOnRQnz59tHTpUkoTcJUcSc7Uy1/HavPRs5KkmpX8NC66vjrVCzM3GAAAgJO4XGmyWq3y9vaWt7e3bZvFYlFiYqK2bdumiIgIu/s3aNBAK1eudHg/Fov9n4CrcNWxmZVboMWb4/RezDHlWw35eFo0tF1d/bNtbfl5e5odD1eJq45PQGJ8wrUxPl1TSV8PlytNrVq1kp+fn2bPnq1Ro0YpOTlZb7/9tu12f39/u/v7+fkpMzPT4f2EhgZJksLCgv5eYMBJXGVsGoahL3claMrnu3X8bJYkqWujKnr+9qYKDwswOR3M4irjE7gUxidcGePTPblcaapYsaIWLVqkGTNmqEuXLqpbt6769u2r33//XZ6ensrIyLC7f3Z2tgICHP/glpycptDQICUlpckwSis98PdZLIVvqK4wNuNTsjTr61j9dDhFklSjoq+eiK6vzvXDZDGsSkxMMzcgrjpXGp/AnzE+4coYn66p6HW5EpcrTbm5ucrPz9e7774ry/njZR988IEaNGigFi1a6J133rG7f2xsrBo2bOjwfooGq2GIgQuXZObYzC+w6p3N8VqyJU65BYa8PS0a0qa27m9X1zYVj7835RvvnXBljE+4Msane3LJJccffPBBrVy5UoZhaOfOnZo/f77uu+8+de/eXYmJiVqyZIny8vK0adMmrV69Wv379zc7MlBmxKdk6cFl27Xw56PKLTDUPjxE//tnaz3S6VrOXQIAAOWSxTBcr+vGxMRoxowZOnz4sMLCwnTfffdpyJAhkqTff/9d06ZN0/79+xUaGqqRI0eqX79+Du8jKSlNYWFBSkzkEClci8UiVa589cemYRj6fFeCZn0dq6w8qyr6eWn8zQ3UvVEV21FfwKzxCZQE4xOujPHpmopelyvezxVL09VAaYKrMuNNNS07X9PXH9CG/WckSa3rVNLztzRS9Ypccwn2+EcfrozxCVfG+HRNJS1NLndOE4Cra9uxVD23Zq9OpeXI08Oi4TeE659t68jTg6NLAAAAEqUJKLfyC6xatClOSzbHyWpItYP9NLVXYzWtUdHsaAAAAC6F0gSUQ8fOZum5NXv1+8nCJcN7N62mJ6PrK8CHtwQAAIA/4xMSUM6s2Z2glzbGKiO3QIG+nprYraF6NK5qdiwAAACXRWkCyon0nHzN3HBAX+4tXOzh+loV9UKvxqrBYg8AAADFojQB5cD244WLPZw4lyNPi/TwDeEaGlWXxR4AAABKgNIElGH5VkPvbIrT25uOqsCQalby05RejdWiJos9AAAAlBSlCSijTqRm67k1e7X9xDlJ0q3XVdVTNzdQoC9/7QEAABzBpyegDPpq72lNX39AGbkFCvDx1PhuDXTrddXMjgUAAOCWKE1AGZKRm69ZG2P1xe7TkqTmNSpqym2NVKuSv8nJAAAA3BelCSgjdp48p0lf7NXx1Gx5WKQH2tXVgx3C5cViDwAAAH8LpQlwcwVWQ//dEq+FPx1RgSFVD/LVlF6NdX3tSmZHAwAAKBMoTYAbO3UuW8+t3adtx1IlST0aVdGEbg0V5MdfbQAAgNLCJyvATW3Yd0bT1x9QWk6+Knh76qmbG6hXk6qyWJiOBwAAUJooTYCbycwt0OxvYvXZzgRJUtPqQZrSq7HqhLDYAwAAgDNQmgA3svtUmp5ds1dxKVmySLq/XR093CFcXp4eZkcDAAAosyhNgBuwGobeizmmN388ogKroaqBPnqhV2O1rhNsdjQAAIAyj9IEuLjTaTmavG6ffok7K0m6OaKyJnZrqEr+3uYGAwAAKCcoTYAL++ZAoqZ9tV+p2fny8/LQk9H1dXuz6iz2AAAAcBVRmgAXlJmbr2lf7dfHO05Jkq6rFqgpvRorPLSCyckAAADKH0oT4GIOJWZown+36tCZDFkkDWlbWyM6XiNvFnsAAAAwBaUJcCEZufl6/ONdOp6arSqBPvr3rY3Utm6I2bEAAADKNUoT4EJe/eaQjqdmq1awv5bc21LB/j5mRwIAACj3mO8DuIjvYhP16c5TskiafVdLhVSgMAEAALgCShPgApIycjXtqwOSpH+0ra329cJMTgQAAIAilCbAZIZhaOpX+5WSlaeGVQL0SMdrzI4EAACAC1CaAJN9/Psp/XAoWd6eFr1wa2P5ePHXEgAAwJXw6QwwUVxKll795qAkaVSna9WgSoDJiQAAAPBnlCbAJPlWQ5PX7lV2vlVt6lTSPa1rmR0JAAAAl0BpAkzyzuY47TyZpkBfT02+pZE8LBazIwEAAOASKE2ACXadPKe3fz4qSRp/c0NVr+hnciIAAABcDqUJuMqy8gr03Np9KjCkHo2q6JbrqpodCQAAAMWgNAFX2evfHVJcSpaqBvpofLcGZscBAADAFVCagKvox0PJWrX9pCRp8i2NVNHP2+REAAAAuBJKE3CVpGTm6oUv90mS7mlVS1HhISYnAgAAQElQmoCrwDAMTV9/QMmZebo2rIJGdrrG7EgAAAAoIUoTcBWs3pWgb2OT5OVh0ZRejeXn7Wl2JAAAAJQQpQlwsmNnszT764OSpBEdr1GjqoEmJwIAAIAjKE2AExVYDT2/dp8y8woUWaui/tGmttmRAAAA4CBKE+BE78bEa/uJcwrw8dTztzaWp4fF7EgAAABwEKUJcJK9CWla8NNRSdKT0fVVs5KfyYkAAADwV1CaACfIzivQc2v2qcBqqGvDyrqtSTWzIwEAAOAvojQBTjD3+8M6nJypsAAfPd2toSwWpuUBAAC4K0oTUMo2H0nR8m0nJEnP9YxQcAVvkxMBAADg76A0AaUoNStP//5ynyRpQMsauuHaUJMTAQAA4O/yKsmdhgwZcsXpRe+++26pBALclWEYmrkhVmfScxUe4q9/da5ndiQAAACUghIdaWrXrp2ioqJUs2ZN7d69W9ddd5169uypli1bat++fbr22mudnRNweWv3nNaG/Wfk6WHRC70ay8/b0+xIAAAAKAUlOtI0evRoSdK9996rhQsXqlWrVrbbevbsqWeffdY56QA3cepctl7aGCtJeqh9XTWpHmRyIgAAAJQWh85p2rNnj1q2bGm3rVGjRjpy5EhpZgLcitUw9Py6fcrILVDzGkEa2q6u2ZEAAABQihwqTfXr19eSJUvsts2fP1+NGzcuzUyAW1n6yzFtjU+Vv7eHXujVWF4eLC8OAABQlpRoel6Rp59+WiNGjNB7772n6tWr68SJE7JarXr77bedlQ9waQfOpOvNH49IksZ2qa/awf7mBgIAAECpc6g0tWrVSl999ZW+/fZbJSQkqHr16oqOjlZQEOdvoPzJybfquTX7lFdg6Kb6YbqjeXWzIwEAAMAJHCpNkhQcHKyIiAhVqFBBXbp0UVpamjNyAS7vzR+OKDYxQ6EVvPVMj4ZXXJYfAAAA7smhc5qSkpJ0991366677tL48eMVHx+vbt26adu2baUaateuXRo8eLDatGmjTp06aerUqcrNzZUkbd++XQMHDlRkZKSio6O1YsWKUt03UBK/xJ3VB1uPSZKe6RGh0Ao+JicCAACAszhUmqZPn66IiAjFxMTIy8tL9evX17Bhw/TSSy+VWiCr1arhw4erZ8+e2rJli1auXKkffvhBixYtUmpqqoYNG6a+ffsqJiZG06ZN04wZM7Rjx45S2z9wJWnZ+Xp+3T4Zkvo2r66b6oeZHQkAAABO5FBp2rRpkyZOnCh/f3/bVKSHHnpIsbGxpRYoNTVVZ86ckdVqlWEYhSE9POTv76+vvvpKwcHBGjx4sLy8vNShQwf16dNHS5cuLbX9A1fy0texSkjLUZ1gPz3epb7ZcQAAAOBkDpUmb29vZWdnS5Kt0GRkZCggIKDUAoWEhGjo0KF68cUX1bx5c3Xu3FnXXHONhg4dqgMHDigiIsLu/g0aNNDevXtLbf9Acb7ae1rr9pyWh0X6962NVcHH0+xIAAAAcDKHFoKIjo7WuHHjNGnSJFksFiUlJWnq1Knq3LlzqQWyWq3y8/PTs88+qwEDBujo0aMaPXq05syZo4yMDPn72y/p7Ofnp8zMTIf3U3TOPufuo6ROp+XoxY2FR1UfaFdXLWpVdMp+GJtwZYxPuDLGJ1wZ49M1lfT1cKg0PfHEE5o4caJuueUWSVKnTp3UuXNnvfDCCw4HvJz169fryy+/1Lp16yRJDRs21KhRozRt2jT16dPnotX6srOz/9KRrtDQwmXSw8JYLh1XZrUaeuyT3TqXna+WtStpfJ+m8vZ06ECtwxibcGWMT7gyxidcGePTPTlUmgICAjRnzhwlJyfr2LFjql69uqpWrVqqgU6ePGlbKc8W0stL3t7eioiI0I8//mh3W2xsrBo2bOjwfpKT0xQaGqSkpDSdn2kIXNayX4/rh9hE+Xp56NkeDZWakuG0fVkshW+ojE24IsYnXBnjE66M8email6XK3H4Ok2nT59WXFycDMPQ0aNHdfToUUlS27ZtHU95CZ06ddLs2bM1f/58Pfzwwzpx4oTefPNN9enTR927d9esWbO0ZMkSDR48WFu3btXq1as1b948h/dTNFgNQwxcFOtQUobe+L/DkqR/da6n8JAKV2XMMDbhyhifcGWMT7gyxqd7cqg0vffee5o5c6YKCgrstlssFu3Zs6dUAjVo0EALFizQa6+9prfeektBQUG6/fbbNWrUKPn4+Gjx4sWaNm2a5syZo9DQUE2aNEnt27cvlX0Df5ZXYNWzX+xVTr5VN1wbogEta5gdCQAAAFeZxTBK3nW7deumhx9+WP3795eXl8MHqVxKUlKawsKClJjIIVJc3n++P6wlW+JVyc9Ly+5rrcqBvk7fp8UiVa7M2IRrYnzClTE+4coYn66p6HW5EoeaT3JysgYOHCgPD+eeAA+4gt+OperdmHhJ0tM9Iq5KYQIAAIDrcaj9REVFafPmzc7KAriM9Jx8TV67V1ZD6t20mqIbVjY7EgAAAEzi0JGmatWqafjw4WrXrp0qV7b/EDljxoxSDQaY6ZVvDurEuRzVrOirJ7rWNzsOAAAATORQacrNzdVtt93mrCyAS/j6QKJW70qQRdLztzZWoK97n78HAACAv8ehT4McTUJZl5ieo+lf7Zck/TOqjiJrVzI5EQAAAMxWotK0cOFCDRs2THPnzr3sfUaPHl1qoQAzGIahKV/tV2p2viKqBGj4DeFmRwIAAIALKFFpiomJ0bBhwy67CITFYinVUIAZ1u45rZ8Op8jH06IXejWWtyerRAIAAKCEpWnRokWSCi9uC5RFGbn5euP/DkuSHuoQrvqVA0xOBAAAAFfh8BnumzZtUkJCgoquiZuXl6d9+/Zp0qRJpR4OuFoWb4pTYkau6gT7aXDr2mbHAQAAgAtxqDRNnTpVy5YtU0BA4f+FLygoUEZGhm688UanhAOuhiPJmfpg63FJ0tiu9eXjxbQ8AAAA/MGh0rR27Vq9//77ysrK0meffabp06frxRdfVGZmprPyAU5lGIZe+eag8q2GOl4bqk71wsyOBAAAABfjUGnKysrS9ddfrzNnzmjXrl2yWCwaPXq0evXq5ax8gFP938Fk/XwkRd6eFo3lIrYAAAC4BIfmIVWvXl1JSUmqUqWKTp06pby8PPn5+Sk9Pd1Z+QCnycm36tVvD0qS7m1dW3VD/E1OBAAAAFfk0JGmzp07a+jQofrvf/+rtm3b6umnn5avr6+uueYaJ8UDnOf9X+J1PDVbVQN99EC7umbHAQAAgIty6EjT2LFjdccdd8jb21vPPfecUlJSFBsbqylTpjgrH+AUp85l653N8ZKkMTfVUwUfT5MTAQAAwFU5dKTJ29tbDz30kCQpKChIb731llNCAc72+neHlZNvVWTtSurRuIrZcQAAAODCSlSaJk6ceMX7zJgx42+HAa6GX+LOasP+M/KwSE92rS+LxWJ2JAAAALgwLkiDciXfaujlb2IlSf1a1FBE1UCTEwEAAMDVlehIE0eRUFas/O2EDiZmqpKfl0Z0vMbsOAAAAHADDh1pys/P15w5c3TzzTerRYsW6tmzp95++21nZQNKVXJmrhb8dESSNLLTNark721uIAAAALgFhxaCeOmll/Ttt99q+PDhqlGjhuLj47V48WLl5ORo5MiRzsoIlIp53x9Rek6BGlcN1B3Na5gdBwAAAG7CodL02Wef6cMPP1Tdun9c06Z9+/a67777KE1wabtOpemznackSU9G15enB4s/AAAAoGQcmp5nGIaqVLFfnrl27doyDKNUQwGlyWoYmrUxVoakXk2qqmWtSmZHAgAAgBtxqDQNHjxYkyZN0rlz5yRJOTk5mjlzpgYMGOCUcEBp+GJXgnadSlOAj6cevfFas+MAAADAzTg0PW/VqlVKSEjQ2rVrValSJaWlpSk/P1+StGDBAtv99uzZU7opgb8oPSdfc78/LEl6sH1dVQ70NTkRAAAA3I1DpWnWrFnOygE4xaKfjyo5M0/hIf66u1Uts+MAAADADTlUmipVqqRGjRpdtP3zzz9X7969Sy0UUBoOJWVo+bYTkqQnouvL25NrOQMAAMBxDn2KHDRokFasWGH7PjMzU+PHj9ekSZNKPRjwdxiGoZe/PqgCq6HO9cPU4ZpQsyMBAADATTl0pOm1117TxIkTFRMTowEDBmjSpEmqVKmSPvroI2flA/6Sbw4kKiburHw8LXqsSz2z4wAAAMCNOXSkqUuXLvr000/1yy+/6L777lPr1q21fPly1avHh1K4juy8Ar367SFJ0pC2dVQ72N/kRAAAAHBnDpWm06dP69lnn1V2drbuvvturV27Vm+99RbXaYJL+e+WeJ1Ky1H1IF8NjapjdhwAAAC4OYdKU+/evZWfn69PP/1UkydP1uLFi7V8+XLdc889zsoHOOR4apbejYmXJD3WpZ78vD1NTgQAAAB351Bpeuihh/T222+rSpUqkqRWrVrpk08+Ua1aLOUM1/Dat4eUW2CoTZ1Kim5Y2ew4AAAAKAMcKk3Dhg2TJKWmpmrnzp2yWq3y9fXV7NmznRIOcMTmIyn6NjZJnhbpyegGslgsZkcCAABAGeBQacrMzNQTTzyhdu3a6R//+IeOHDmi7t2769ChQ87KB5RIXoFVL38TK0kaGFlL9SsHmJwIAAAAZYVDpenFF19UZmam1q5dK29vb9WpU0ddu3bVtGnTnJUPKJHl207oSHKWQvy9NaxDuNlxAAAAUIY4dJ2mb775RqtXr1alSpVksVjk7e2tCRMm6KabbnJWPuCKEtNz9NbPRyVJo2+8VkF+Dg1rAAAAoFgOHWmyWq3y8fGRJNsy4xduA8ww9/vDysgtUNPqQerdrJrZcQAAAFDGOFSa2rdvrxdeeEFZWVm2k+xfe+01RUVFOSUccCXbj6fqi92nJUnjouvLg8UfAAAAUMocKk0TJ07UwYMH1bZtW6WlpSkyMlIxMTEaP368s/IBl1VgNTT7m4OSpD5Nq6lpjYomJwIAAEBZ5NDJH2FhYVq+fLl+//13HT9+XNWrV1eLFi3k6ckFRHH1fbbzlPYkpCvAx1OjbrzW7DgAAAAooxw+Y95isahFixZq0aKFM/IAJXIuO0/zfjgiSRp2Q7jCAjivDgAAAM7h0PQ8wFUs+PGozmbl6dqwCrrr+ppmxwEAAEAZRmmC2zlwJl0rt5+QVLj4g5cnwxgAAADOw6dNuBXDMDTr64OyGtLNEZXVtm6I2ZEAAABQxjlUmqKjozV37lwdP37cWXmAYq3fd0bbjqXK18tD/+pcz+w4AAAAKAccKk1PPfWUdu7cqZ49e2ro0KFavXq1cnJynJUNsJOZW6DXvzskSbovqo5qVPQzOREAAADKA4dK0y233KL58+fru+++U+fOnfXuu+/qxhtv1PPPP6/ff//dWRkBSdKSLXE6nZ6rmhV9NaRNbbPjAAAAoJz4S+c0hYWF6Y477lC/fv1Uq1YtrVq1SsOHD1e/fv20Z8+e0s4IKD4lS+//ckyS9HiX+vLz5tpgAAAAuDocuk5Tbm6uNm7cqE8//VQ//PCDGjZsqDvvvFN9+vRRpUqVNHfuXI0ePVobN250Vl6UU698e1B5BYbah4eoc4Mws+MAAACgHHGoNN1www3y9PRU7969tXz5cjVt2tTu9l69eumTTz4pzXyAfjyUrB8OJcvTw6InutaXxWIxOxIAAADKEYdK0wsvvKBu3brJx8fHbnt6eroCAwPVoEEDff3116UaEOVbbr5Vs7+JlSTd06qWrgmrYHIiAAAAlDcOndP0/PPPX1SYJKlLly6llQew88HWY4o/m62wAB892L6u2XEAAABQDl3xSNPRo0f13HPPyTAMpaen65///Kfd7enp6apYsWKpBfrss880efJku215eXmSpJ07d2r79u2aOnWqYmNjFRISokceeUQDBw4stf3DdZxOy9HizXGSpEdvvFaBvg4dGAUAAABKxRU/hYaHh6tHjx5KSUnRr7/+qqioKLvbfXx8FB0dXWqBbr/9dt1+++227xMSEtS/f3+NGzdOqampGjZsmMaMGaNBgwYpJiZGo0aNUqNGjdSiRYtSywDXMOf/Dikrz6rmNSrq1iZVzY4DAACAcqpE/+t+8ODBkqTatWurb9++zsxjxzAMjRs3Tl26dNEdd9yhFStWKDg42JanQ4cO6tOnj5YuXUppKmO2HUvVl3vPyCLpqZvry4PFHwAAAGCSEpWmzz//XL1795aky66O54wy9emnnyo2Nlbz5s2TJB04cEARERF292nQoIFWrlxZ6vuGeQqshmZ9Xbj4Q98W1dW4WpDJiQAAAFCelag0zZ8/X71799acOXMuebvFYin10mS1WvXmm29qxIgRCgwMlCRlZGTI39/f7n5+fn7KzMx0+PGLDlxwAMP1fLzjpA6cyVBFPy+N6nRtuXuNGJtwZYxPuDLGJ1wZ49M1lfT1KPGRJklXdTnxzZs36/Tp0xowYIBtm7+/v9LS0uzul52drYCAAIcfPzS08OhFWBhHMVxJckau5v90VJL0ZM9GalA31ORE5mFswpUxPuHKGJ9wZYxP91Si0hQTE1Ps7RaLRW3atCmVQEW+/PJLde/eXRUq/HFdnoiICP34449294uNjVXDhg0dfvzk5DSFhgYpKSlNhvG346KUTF9/QKlZeWpYJUA96ocqMTHtyj9UxlgshW+ojE24IsYnXBnjE66M8email6XKylRaRoyZMgVdmbRnj17SpashLZu3XrR8ubdu3fXrFmztGTJEg0ePFhbt27V6tWrbec8OaJosBqGGLguYm9Cmj7eflKS9ETX+vK0WMr1a8PYhCtjfMKVMT7hyhif7qlEpWnv3r3OznGRY8eOqWpV+2WmQ0JCtHjxYk2bNk1z5sxRaGioJk2apPbt21/1fChdhmFo1tcHZUjq0aiKWtcJNjsSAAAAIKmEpenUqVOqXr26Tpw4cdn71KxZs9RCSdK2bdsuub158+ZatmxZqe4L5lu757R2nDgnPy8Pjelcz+w4AAAAgE2JSlOvXr3066+/Kjo6WhaLRcafjik6Y3oeyg/DMLRkc7wk6YH2dVUtyNfkRAAAAMAfSlSavvjiC0nSxo0bnRoG5dPBpEwdTs6Ut6dFA68v3SOWAAAAwN9VotJUo0YNSVKtWrVktVq1Y8cOJSQkqHbt2mratKlTA6Ls27DvjCSpwzWhCvQt0ZAEAAAArhqHPqEePXpUw4cP17FjxxQcHKyUlBQ1bdpUc+fOvWjRBqAkDMPQxv2FpenmiMompwEAAAAu5uHInadMmaL27dvrl19+0Q8//KDNmzerfv36euGFF5yVD2XcwaRMHUnOkrenRTfVDzM7DgAAAHARh440/f7775o3b558fHwkSYGBgXruuefUpUsXZ2RDOcDUPAAAALg6h4401apVS3FxcXbbTp06peDg4NLMhHKCqXkAAABwByX6X/uffPKJJKlVq1Z6+OGH9eCDD6pWrVo6ffq0Fi9erG7dujkzI8qog4mFU/N8mJoHAAAAF1ai0jRnzhzb1xaLRYsXL7a7fd26dRo3blzpJkOZt+H8Uab2TM0DAACACyvRJ9Wvv/7a2TlQzjA1DwAAAO7C4f+9Hx8fr4SEBBmGIUnKy8vT/v37NXTo0NLOhjKMqXkAAABwFw6VpgULFujVV1+VxWKRVHi0wGKx6LrrrqM0wSFMzQMAAIC7cOjT6gcffKA5c+bIx8dHX3/9tcaOHaspU6aoRo0azsqHMsgwDNtS490aMTUPAAAArs2hJcfPnTunHj16qHHjxtq5c6eCg4P1zDPPaM2aNc7KhzLoYGKmjqYUTs27sR5T8wAAAODaHCpNVatWVXp6uqpVq6Zjx47JMAyFhoYqNTXVWflQBjE1DwAAAO7EoU+sbdu21ZgxY/Taa6+pSZMmeuWVV+Tr66tq1ao5Kx/KGKbmAQAAwN04dKRpwoQJCg8PV35+vp5++mlt3LhRy5cv19NPP+2sfChjmJoHAAAAd+PQkabAwEBNnjxZkhQaGsq5THDY+vNT8zowNQ8AAABuwqFPrXPnzr3sbaNHj/7bYVC2GYahjeen5t3M1DwAAAC4CYdK0+bNm+2+P3v2rA4ePKhbbrmlVEOhbGJqHgAAANyRQ6Xpvffeu2jbp59+elGZAi6FqXkAAABwRw4tBHEpd9xxhzZu3FgaWVCGMTUPAAAA7upvl6YtW7aoQoUKpZEFZVhsYgZT8wAAAOCWHJojFR0dLYvFYvs+Ly9PiYmJeuSRR0o9GMqWDfsTJTE1DwAAAO7HoU+vjz76qN33Hh4eql+/vpo1a1aqoVC2MDUPAAAA7syh0vTVV19p1qxZCgwMdFYelEFMzQMAAIA7c+icpm3btsnHx8dZWVBGMTUPAAAA7syhT7C9e/fWmDFj1KdPH1WpUsXu/Ka2bduWeji4P8MwtOH81LxujaqYnAYAAABwnEOl6f3335ckffvtt3bbLRaL9uzZU2qhUHbEJmYormhqXv1Qs+MAAAAADnOoNO3du9dZOVBGXTg1L8CHqXkAAABwPw5/is3KylJqaqqsVqukwmXH9+/fr+7du5d6OLg3puYBAACgLHCoNK1atUpTpkxRTk6O3fawsDBKEy7C1DwAAACUBQ6Vpvnz5+uxxx5TQECAYmJidN9992nWrFnq2LGjs/LBjRUdZbrhWqbmAQAAwH05tOT4mTNndN9996lDhw6Ki4tT06ZNNX36dK1YscJZ+eCmDMOwnc90cwRT8wAAAOC+HCpNYWFhysvLU40aNXT48GFJUs2aNZWUlOSUcHBfTM0DAABAWeFQaWrRooWee+45ZWdn65prrtH//vc/ffzxxwoODnZSPLgrpuYBAACgrHDo0+yECRP07LPPKiMjQ+PGjdOIESOUnZ2tGTNmOCsf3BBT8wAAAFCWOFSaQkNDtXDhQklS1apVtWnTJuXl5cnf398p4eCemJoHAACAssSh6XmdOnXS9OnTtX//fkmSl5cXhQkXYWoeAAAAyhKHStNLL72kM2fOaODAgRo4cKCWL1+ujIwMZ2WDG2JqHgAAAMoah0pT586d9eqrr+r777/XnXfeqY8//lg33nijJk6c6Kx8cDMHzjA1DwAAAGXLX5o7VbFiRd100006d+6cTp8+rU2bNpV2LripjfuZmgcAAICyxaFPtenp6Vq7dq0++eQT7dixQ507d9bkyZN14403Oisf3AhT8wAAAFAWOVSaOnbsqOrVq6t///56/fXXVblyZWflghtiah4AAADKIodK04IFC9S+fXtnZYGbY2oeAAAAyiKHFoKgMOFyLpya142peQAAAChDHCpNwOVcODWvE1PzAAAAUIZQmlAqmJoHAACAsqpEpWnIkCGSpFWrVjk1DNwTU/MAAABQlpWoNO3cuVPnzp3TtGnTnJ0HboipeQAAACjLSjSPqlWrVmrXrp0k6brrrrvkffbs2VN6qeBWNjA1DwAAAGVYiT7hzp07V7t379YDDzygRYsWOTsT3IhhGNrI1DwAAACUYSUqTf7+/mrdurVmz56tqKgoZ2eCG2FqHgAAAMo6h1bPu/nmm/Xf//5XvXr1UsuWLdWtWzfNnz9fhmE4Kx9cHFPzAAAAUNY59Cn33Xff1TvvvKNhw4apdu3aiouL01tvvSUPDw8NGzas1EKdPXtW06dP13fffSer1aq2bdvq+eefV9WqVbV9+3ZNnTpVsbGxCgkJ0SOPPKKBAweW2r5RckzNAwAAQHng0JGmZcuWad68ebr33nt100036R//+IfmzZun5cuXl2qoRx99VJmZmVq/fr2++eYbeXp66tlnn1VqaqqGDRumvn37KiYmRtOmTdOMGTO0Y8eOUt0/Smb/+al5vl4eTM0DAABAmeXQkabTp0+rcePGdtsaN26ss2fPllqgnTt3avv27frpp58UGBgoSZoyZYrOnDmjr776SsHBwRo8eLAkqUOHDurTp4+WLl2qFi1alFoGlEzRBW07XBPC1DwAAACUWQ590g0PD9f69evVs2dP27b169crPDy81ALt2LFDDRo00Icffqj//e9/ysrK0o033qjx48frwIEDioiIsLt/gwYNtHLlSof3Y7HY/wnH2E3Na1SF32MpYmzClTE+4coYn3BljE/XVNLXw6HSNHLkSD322GNat26d6tSpo7i4OG3cuFFz5sz5KxkvKTU1Vfv27VOzZs308ccfKzs7W0899ZTGjx+vypUry9/f3+7+fn5+yszMdHg/oaFBkqSwsKBSyV3e7DqRapua1zcqXIG+HGkqbYxNuDLGJ1wZ4xOujPHpnhz6pNutWze99dZb+vjjj7Vr1y7VqlWr1KfG+fj4SJKeeeYZ+fr6KjAwUI899pjuuusu9evXT9nZ2Xb3z87OVkBAgMP7SU5OU2hokJKS0sTif45bufmoJOmGa0OUnZal7DSTA5UhFkvhGypjE66I8QlXxviEK2N8uqai1+VKHD480L59e7Vv3/4vhSqJBg0ayGq1Ki8vT76+vpIkq9UqSbruuuv0wQcf2N0/NjZWDRs2dHg/RYPVMMTAdZBhGNqwr/B8pm4RVfj9OQljE66M8QlXxviEK2N8uieHVs+7Gm644QbVqVNHTz/9tDIyMpScnKxXX31V3bp1U+/evZWYmKglS5YoLy9PmzZt0urVq9W/f3+zY5cr+89kKP5sduGqefXCzI4DAAAAOJXLlSZvb2+999578vT0VM+ePdWzZ09Vr15d06dPV0hIiBYvXqx169apXbt2mjRpkiZNmuTUI1+42IWr5lXw8TQ5DQAAAOBcLnn2frVq1fTqq69e8rbmzZtr2bJlVzkRilw4Na97Iy5oCwAAgLLP5Y40wbUxNQ8AAADlTamUpueff740HgZuoOgo0w3XhjI1DwAAAOVCqZSm48ePl8bDwMUVXtC2aNW8yianAQAAAK6OUilNixYtKo2HgYtjah4AAADKI4dK0yOPPHLJ7f/4xz9KJQxcG1PzAAAAUB5dcfW8Y8eO6ZNPPpEk/fDDD5o7d67d7enp6dq3b59TwsF1MDUPAAAA5dUVS1PNmjV14MABJScnq6CgQJs3b7a73dfXV5MnT3ZaQLiG/aeZmgcAAIDy6YqlycPDQ6+//rokadKkSZo6darTQ8H1bNjP1DwAAACUTw5d3Hbq1KnKzc1VcnKyrFar3W01a9Ys1WBwHUzNAwAAQHnmUGlat26dnn32WaWnp9u2GYYhi8WiPXv2lHo4uAam5gEAAKA8c6g0zZkzR4MHD9add94pLy+HfhRujKl5AAAAKM8caj4nT57U6NGjKUzliGEYttLE1DwAAACURw5dp6lp06aKjY11Vha4oP2nM3SMqXkAAAAoxxw6ZNSqVSsNHTpUt9xyiypXtj/qMHr06FINBtfA1DwAAACUdw6Vpm3btqlhw4Y6ePCgDh48aNtusVhKPRjMx9Q8AAAAwMHS9N577zkrB1wQU/MAAAAAB89pkqSDBw9q6tSpGj16tFJSUvT+++87IxdcwPrzR5k6MjUPAAAA5ZhDpenHH3/UwIEDlZKSop9++knZ2dn6z3/+o4ULFzorH0xy4QVtb2ZqHgAAAMoxh0rTK6+8oldffVWzZ8+Wp6enatSooYULF2r58uXOygeTMDUPAAAAKORQaTp69KhuuukmSX8s/tC8eXOlpqaWfjKYiql5AAAAQCGHSlPNmjX166+/2m37/fffVaNGjVINBXMxNQ8AAAD4g0Or5w0fPlyPPPKI7rnnHuXl5WnRokV67733NHbsWGflgwn2nU5nah4AAABwnkOl6bbbblNgYKCWLl2qmjVratOmTXrmmWfUs2dPZ+WDCTbsT5TE1DwAAABAcrA0SVLt2rX1yiuvKDAwUNu2bVPFihWdkQsmYWoeAAAAYM+hc5rWrl2rvn376siRI5Kk3377TQMHDtR3333njGwwAVPzAAAAAHsOHWmaO3eu5s2bp2bNmkmS7r//fjVo0ECzZs1S586dnRIQVxdT8wAAAAB7Dh1pOnnypG688Ua7bZ06ddKJEydKNRTMceHUvG6NqpicBgAAAHANDpWmWrVq6fvvv7fb9vPPP6tmzZqlGgrmsJ+aF2p2HAAAAMAlODQ9b9iwYRo1apR69OihWrVq6cSJE1q/fr1efPFFZ+XDVXTh1Dx/b6bmAQAAAJKDpalPnz6qWrWqPvnkE+3atUs1atTQ4sWL1apVK2flw1ViGIY27GNqHgAAAPBnDpWmRx55RLNmzVK7du2clQcm2Xc6XcdTmZoHAAAA/JlD5zRt27ZNPj4+zsoCEzE1DwAAALg0h4409e7dW2PGjFGfPn1UpUoVWSwW221t27Yt9XC4OpiaBwAAAFyeQ6Xp/ffflyR9++23dtstFov27NlTaqFwdTE1DwAAALg8h0rT3r17nZUDJlq/r3BqXqd6TM0DAAAA/syhc5okKTc3V+vXr9eSJUuUlZVFkXJzF17Q9uYIpuYBAAAAf+bQkaa4uDg98MADysvL07lz59S5c2f1799fc+fOVdeuXZ2VEU7E1DwAAACgeA4daZo2bZr69eunb7/9Vl5eXrr22ms1depUzZkzx1n54GQ/Hk6WJHW4JoSpeQAAAMAlOFSafvvtNz300EOyWCy2lfPuuOMOxcfHOyUcnG/L0bOSpHbhIeYGAQAAAFyUQ6UpKChIiYmJdtvOnDmjSpUqlWooXB2ZuQXaceKcJEoTAAAAcDkOlaY+ffpo9OjR+vHHH2W1WrVjxw49+eSTuu2225yVD0607Xiq8q2Galb0Ve1gP7PjAAAAAC7JoYUgRo4cqezsbI0ePVpZWVn65z//qQEDBmj06NHOygcn2nI0RZLUNjzE7kLFAAAAAP7gUGny9vbW+PHjNX78eCUnJyskhA/b7mzz+dLE1DwAAADg8kpcmubOnatdu3apU6dOGjx4sEJDWZ7anSVm5OpgYqYsktrWCTY7DgAAAOCySnRO00svvaQPPvhA3t7emjNnjhYuXOjsXHCyoql5jaoGKriCt8lpAAAAANdVotL0+eef67///a/mzJmjOXPmaPXq1c7OBSfbEndWkhQVHmxqDgAAAMDVlag0paWlqWHDhpKk1q1bKyEhwamh4FyGYdiONEVxPhMAAABQrBKVJg+PP+7m5eXQ2hFwQUeSs3QmPVe+Xh66vhbX2AIAAACKU6LSZBiGs3PgKipaNa9lzYry9XLoUl0AAABAuVOiw0b5+fn65JNPbN/n5eXZfS9Jffv2LcVYcCaWGgcAAABKrkSlqXLlypozZ47t+5CQELvvLRYLpclN5BdY9Wt8qiQWgQAAAABKokSl6euvv3Z2DlwlO0+mKTOvQJX8vBRRNdDsOAAAAIDL44SWcmZLXOHUvLZ1Q+RhsZicBgAAAHB9Llma1qxZoyZNmigyMtL237hx4yRJ27dv18CBAxUZGano6GitWLHC5LTuZfPRs5KkdkzNAwAAAErEJdcP//3333XHHXdoxowZdttTU1M1bNgwjRkzRoMGDVJMTIxGjRqlRo0aqUWLFialdR/pOfnadfKcJKndNSwCAQAAAJSESx5p+v3339WsWbOLtn/11VcKDg7W4MGD5eXlpQ4dOqhPnz5aunSpCSndz9b4VBUYUp1gP9Wo6Gd2HAAAAMAtuNyRJqvVql27dsnf319vvfWWCgoK1LlzZz355JM6cOCAIiIi7O7foEEDrVy50uH9FJ3OU55O6yk6n6ldeEi5et7upjyOTbgPxidcGeMTrozx6ZpK+nq4XGlKTk5WkyZN1LNnT82ZM0cpKSkaP368xo0bpypVqsjf39/u/n5+fsrMzHR4P6GhQZKksLCgUsntDrYeK1xqvFvzGqpcufw8b3dVnsYm3A/jE66M8QlXxvh0Ty5XmipXrmw33c7f31/jxo3TXXfdpX79+ik7O9vu/tnZ2QoICHB4P8nJaQoNDVJSUpoM42/HdnkJaTk6eCZDHhapUbCfEhPTzI6Ey7BYCt9Qy8vYhHthfMKVMT7hyhifrqnodbkSlytNe/fu1eeff64nnnhClvPHy3Jzc+Xh4aEWLVrov//9r939Y2Nj1bBhQ4f3UzRYDUPlYuBuPlI4Na9J9SAF+nqVi+fs7srL2IR7YnzClTE+4coYn+7J5RaCCA4O1tKlS/XWW28pPz9fJ06c0KxZs3TnnXeqZ8+eSkxM1JIlS5SXl6dNmzZp9erV6t+/v9mxXd7mo4WlKapusLlBAAAAADfjcqWpevXqWrBggTZu3KioqCj1799fzZs313PPPaeQkBAtXrxY69atU7t27TRp0iRNmjRJ7du3Nzu2SzMMQzFxZyVJUeEsNQ4AAAA4wuWm50lSVFSUli1bdsnbmjdvftnbcGmxiRlKzsyTn5eHmteoaHYcAAAAwK243JEmlL7NR89KklrVqSQfL15yAAAAwBF8gi4HttjOZ2JqHgAAAOAoSlMZl5tv1a/nr8/UjvOZAAAAAIdRmsq430+eU06+VaEVvFW/cgWz4wAAAABuh9JUxtmWGg8PsV33CgAAAEDJUZrKuKJFINqFB5uaAwAAAHBXlKYy7Fx2nvacSpPEIhAAAADAX0VpKsN+iTsrQ9K1oRVUNcjX7DgAAACAW6I0lWFb4s5KkqKYmgcAAAD8ZZSmMuzCRSAAAAAA/DWUpjLqeGqWjp3NlqdFal2nktlxAAAAALdFaSqjtpxfNa9ZjYoK8PEyNwwAAADgxihNZdSW81Pz2jE1DwAAAPhbKE1lkNUwFMMiEAAAAECpoDSVQftOpys1O18BPp5qWj3I7DgAAACAW6M0lUFF5zO1rhMsL09eYgAAAODv4BN1GWRbarxusLlBAAAAgDKA0lTGZOcVaPvxVEksAgEAAACUBkpTGbP9+DnlFhiqGuij8FB/s+MAAAAAbo/SVMbYpuaFh8hisZicBgAAAHB/lKYyZgtLjQMAAAClitJUhqRk5mrf6XRJUlRdzmcCAAAASgOlqQwpuqBtwyoBCgvwMTcMAAAAUEZQmsqQousztWWpcQAAAKDUUJrKCMMwbItAsNQ4AAAAUHooTWVE/NlsnUrLkbenRZG1K5kdBwAAACgzKE1lRNFRphY1K8rf29PkNAAAAEDZQWkqI7YwNQ8AAABwCkpTGZBvNfRL/FlJUhSLQAAAAAClitJUBuxNSFN6ToGCfL3UuFqQ2XEAAACAMoXSVAYUnc/Upm6wPD0sJqcBAAAAyhZKUxmw+fz1mdqFB5uaAwAAACiLKE1uLjO3QL+fOCdJiqrLIhAAAABAaaM0ubltx1KVbzVUs6Kvagf7mR0HAAAAKHMoTW5uS1zh+UxR4SGyWDifCQAAAChtlCY3V7QIRBTXZwIAAACcgtLkxhIzcnUwMVMWSW3rBJsdBwAAACiTKE1ubMv5o0yNqgYquIK3yWkAAACAsonS5Ma2MDUPAAAAcDpKk5syDENb4s5KkqK4PhMAAADgNJQmN3U4OVNn0nPl6+Wh62tVMjsOAAAAUGZRmtzUlqNnJUnX16ooXy9eRgAAAMBZ+LTtpmxLjdflfCYAAADAmShNbii/wKpf41MlSe1YBAIAAABwKkqTG9p5Mk2ZeQUK9vdWw6oBZscBAAAAyjRKkxsqmprXtm6wPCwWk9MAAAAAZRulyQ0VLTXejqXGAQAAAKejNLmZ9Jx87Tp5ThIXtQUAAACuBkqTm9kan6oCQ6ob4q8aFf3MjgMAAACUeZQmN7PlgvOZAAAAADgfpcnNFC0CwVLjAAAAwNVBaXIjp85l62hKljwsUps6wWbHAQAAAMoFSpMbKVo1r0n1IAX5eZkbBgAAACgnXLo0FRQUaMiQIZowYYJt2/bt2zVw4EBFRkYqOjpaK1asMDHh1VV0PhOr5gEAAABXj0uXprlz5+qXX36xfZ+amqphw4apb9++iomJ0bRp0zRjxgzt2LHDxJRXh9UwFHP+SFMUi0AAAAAAV43Llqaff/5ZX331lXr06GHb9tVXXyk4OFiDBw+Wl5eXOnTooD59+mjp0qUmJr06DiZmKDkzT35eHmpRs6LZcQAAAIBywyVLU1JSkp555hnNnj1b/v7+tu0HDhxQRESE3X0bNGigvXv3Xu2IV93mo2clSa3qVJK3p0u+bAAAAECZ5HKrCVitVo0bN07333+/GjdubHdbRkaGXYmSJD8/P2VmZjq8H4vF/k9Xt+WCpcbdJTP+GncbmyhfGJ9wZYxPuDLGp2sq6evhcqVpwYIF8vHx0ZAhQy66zd/fX2lpaXbbsrOzFRAQ4PB+QkODJElhYUF/LehVlJNfoG3HUyVJt1xfW5Uru35m/H3uMDZRfjE+4coYn3BljE/35HKl6dNPP9Xp06fVpk0bSYWlSJI2bNigp556Sj/++KPd/WNjY9WwYUOH95OcnKbQ0CAlJaXJMP5+bmf6Je6ssvOsCgvwUainocTEtCv/ENyWxVL4huoOYxPlD+MTrozxCVfG+HRNRa/LlbhcaVq3bp3d90XLjc+cOVMpKSmaNWuWlixZosGDB2vr1q1avXq15s2b5/B+igarYcjlB+7moqXG6wZLsrh8XpQOdxibKL8Yn3BljE+4Msane3KrFQVCQkK0ePFirVu3Tu3atdOkSZM0adIktW/f3uxoTlW0CERUeLCpOQAAAIDyyOWONP3ZzJkz7b5v3ry5li1bZlKaq+9cdp72nCqcjhdVl4vaAgAAAFebWx1pKo9+iTsrQ9K1oRVUNcjX7DgAAABAuUNpcnFMzQMAAADMRWlycVvi/rg+EwAAAICrj9Lkwo6nZunY2Wx5eljUqk4ls+MAAAAA5RKlyYVtOT81r3mNIAX4uPyaHQAAAECZRGlyYVts12diah4AAABgFkqTiyqwGoqJOyuJRSAAAAAAM1GaXNT+M+lKzc5XgI+nmlYPMjsOAAAAUG5RmlzU5iOFU/Na1wmWlycvEwAAAGAWPo27qC3np+a1Y2oeAAAAYCpKkwvKzivQ9uOpklgEAgAAADAbpckFbT9+TrkFhqoG+ig81N/sOAAAAEC5RmlyQZuLlhoPD5HFYjE5DQAAAFC+UZpcUFFpahfO1DwAAADAbJQmF5OSmav9ZzIkSW3rBpsbBgAAAAClydUUXdC2YZUAhQX4mBsGAAAAAKXJ1Ww5elYSq+YBAAAAroLS5EIMw7hgEYhgc8MAAAAAkERpcinxZ7N1Ki1H3p4WRdauZHYcAAAAAKI0uZSio0wtalaUv7enyWkAAAAASJQml7KFpcYBAAAAl0NpchH5VkO/xJ+VVHhRWwAAAACugdLkIvacSlN6ToEq+nmpcdVAs+MAAAAAOI/S5CK2xBVOzWtTJ1ieHhaT0wAAAAAoQmlyEZuLrs/EUuMAAACAS6E0uYDM3AL9fuKcJBaBAAAAAFwNpckFbDuWqnyroZqV/FQ72N/sOAAAAAAuQGlyAUXXZ4qqG2xuEAAAAAAXoTS5gKJFIJiaBwAAALgeSpPJEtNzdDAxUxZJbTjSBAAAALgcSpPJtsSdlSQ1rhaoYH9vc8MAAAAAuAilyWRbzp/P1LYuU/MAAAAAV0RpMpFhGLbrM7Xj+kwAAACAS6I0mehwcqYSM3Ll6+WhlrUqmR0HAAAAwCVQmkxUdJTp+loV5evFSwEAAAC4Ij6pm6jofCaWGgcAAABcF6XJROey8+VhkTrWCzU7CgAAAIDL8DI7QHk2o/d1SsrMVb2wALOjAAAAALgMSpOJqgb5qmqQr9kxAAAAABSD6XkAAAAAUAxKEwAAAAAUg9IEAAAAAMWgNAEAAABAMShNAAAAAFAMShMAAAAAFIPSBAAAAADFoDQBAAAAQDEoTQAAAABQDEoTAAAAABSD0gQAAAAAxaA0AQAAAEAxKE0AAAAAUAxKEwAAAAAUg9IEAAAAAMWgNAEAAABAMShNAAAAAFAML7MDmMVisf8TcBWMTbgyxidcGeMTrozx6ZpK+npYDMMwnBsFAAAAANwX0/MAAAAAoBiUJgAAAAAoBqUJAAAAAIpBaQIAAACAYlCaAAAAAKAYlCYAAAAAKAalCQAAAACKQWkCAAAAgGJQmgAAAACgGOWuNCUlJWnkyJFq06aN2rVrp2nTpik/P9/sWIAkac2aNWrSpIkiIyNt/40bN87sWCjnkpOT1b17d23evNm2bfv27Ro4cKAiIyMVHR2tFStWmJgQ5dmlxufkyZPVrFkzu/fS5cuXm5gS5cnevXt1//33KyoqSh07dtRTTz2l5ORkSbx3urNyV5oee+wxVahQQd9//71Wrlypn3/+WUuWLDE7FiBJ+v3333XHHXdo27Zttv9mzZpldiyUY1u3btWgQYMUFxdn25aamqphw4apb9++iomJ0bRp0zRjxgzt2LHDxKQojy41PqXC99IpU6bYvZcOGjTIpJQoT7Kzs/XQQw8pMjJSP/zwgz7//HOdPXtWTz/9NO+dbq5claajR49qy5YtGjdunPz9/VWnTh2NHDlSS5cuNTsaIKnwH/pmzZqZHQOQJH388cd68skn9fjjj9tt/+qrrxQcHKzBgwfLy8tLHTp0UJ8+fXgvxVV1ufGZm5ur/fv3814KU5w4cUKNGzfWqFGj5OPjo5CQEA0aNEgxMTG8d7q5clWaDhw4oODgYFWrVs22rX79+jpx4oTOnTtnYjJAslqt2rVrl7799lt17dpVN910k5599lmlpqaaHQ3lVKdOnbR+/Xr16tXLbvuBAwcUERFht61Bgwbau3fv1YyHcu5y43Pv3r3Kz8/XnDlzdMMNN6hnz55auHChrFarSUlRntSrV09vvfWWPD09bdu+/PJLNW3alPdON1euSlNGRob8/f3tthV9n5mZaUYkwCY5OVlNmjRRz549tWbNGi1btkxHjhzhnCaYpkqVKvLy8rpo+6XeS/38/HgfxVV1ufGZlpamqKgoDRkyRN99951mzZql9957T4sXLzYhJcozwzD06quv6ptvvtEzzzzDe6ebu/jdpgyrUKGCsrKy7LYVfR8QEGBGJMCmcuXKdofo/f39NW7cON11111KT09XYGCgiemAP/j7+ystLc1uW3Z2Nu+jcAkdO3ZUx44dbd+3aNFC9913n9asWaOHHnrIxGQoT9LT0zVx4kTt2rVL77//vho1asR7p5srV0eaGjZsqLNnzyoxMdG27eDBg6pevbqCgoJMTAYUTil5+eWXZRiGbVtubq48PDzk4+NjYjLAXkREhA4cOGC3LTY2Vg0bNjQpEfCHDRs2aNmyZXbbcnNz5efnZ1IilDdxcXHq37+/0tPTtXLlSjVq1EgS753urlyVpmuuuUatW7fW9OnTlZ6ervj4eM2bN08DBgwwOxqg4OBgLV26VG+99Zby8/N14sQJzZo1S3feeSelCS6le/fuSkxM1JIlS5SXl6dNmzZp9erV6t+/v9nRABmGoRkzZujnn3+WYRjatm2b3n33XVbPw1WRmpqq++67T61atdLbb7+t0NBQ2228d7o3i3Hh/9YuBxITE/XCCy9o8+bN8vDwUN++ffXkk0/anbAHmGXLli165ZVXtH//fvn6+uq2227TuHHj5Ovra3Y0lHONGjXSu+++q3bt2kkqXOlx2rRp2r9/v0JDQzVy5Ej169fP5JQor/48PpctW6Z33nlHCQkJqly5su6//34NHjzY5JQoD9555x3NnDlT/v7+slgsdrdt27aN9043Vu5KEwAAAAA4olxNzwMAAAAAR1GaAAAAAKAYlCYAAAAAKAalCQAAAACKQWkCAAAAgGJQmgAAAACgGJQmAAAAACgGpQkAgHLmyJEjZkcAALdCaQKAMqxRo0YaNmyY/nwd848++kjR0dFO2Wd0dLQ++ugjpzx2SaxZs0YdOnRQ69at9c0335iWw1Xt3r1bvXv3NjsGALgVShMAlHHfffed3nrrLbNjXDUrVqzQbbfdpq1bt6pr165mx3E5aWlpysvLMzsGALgVShMAlHFDhgzR66+/rl9//fWStx87dkyNGjXSsWPHbNveeOMNDRkyRFLhUal7771XL774oqKiotS+fXu99957+vDDD9W1a1e1bt1azz33nN1j7tq1S/369VNUVJQefPBBu+lgcXFxGjFihNq1a6euXbvq1VdfVW5urm1f/fr10wMPPKA2bdpo9erVF+VNSUnRs88+q06dOqldu3YaPny47fEHDBigTZs2admyZerWrdsln++PP/6oAQMGKDIyUtHR0Xr//fdtt23YsEH9+vVTq1at1LNnTy1ZskRWq1WSNGHCBL3wwgsaPny4IiMj1b17d/3888+aMmWK2rZtq44dO2rFihV2v9P33ntPHTt2VOvWrTVu3Dilp6fb9lVU7lq1aqU+ffros88+s3vNZs+ercGDBysyMlK33nqr1qxZY7s9MTFRTz75pDp27KhOnTrpueeesz325s2bFR0drTfffFM33nijoqKi9Oijjyo9PV3x8fF6+OGHJUmRkZHatm2bDhw4oMGDB6tt27bq2rWrxo8fb5cTAEBpAoAyr3v37ho0aJDGjh2rs2fP/qXH2Lp1q6pVq6ZNmzZpzJgxmjFjhjZv3qw1a9ZoyZIlWrlypWJiYmz337Bhg2bMmKHvv/9etWvX1vDhw5Wfn6/MzEwNHTpUDRs21P/93//pgw8+0E8//aQ33njD9rO7du1Snz599NNPP6l79+4XZRkzZozi4uL08ccf67vvvlO9evU0dOhQpaena+XKlWrTpo2GDx+uDRs2XPSzhw8f1ogRI3T33XcrJiZGc+bM0SuvvKLvv/9emzZt0mOPPaaHHnpIW7Zs0SuvvKJ33nlH7777ru3nV61apYcfflhbt25VixYt9OCDD+qaa67Rzz//rOHDh+uFF16wFUBJ+uqrr7R69WqtW7dOR48e1b///W9JheVw5syZmjRpkmJiYvT000/r3//+t9avX2/72Q8//FDPPPOMNm/erB49eui5555TTk6OrFarRo4cKQ8PD3355ZdavXq1Tp8+bVdcjx8/roSEBK1fv14rVqzQtm3b9MEHH6hOnTpatGiRJGnbtm2KjIzUv//9b3Xo0EFbtmzRqlWrtHv3blv5AwAUojQBQDkwfvx4hYaGasKECRed31QSFSpU0H333ScPDw916tRJBQUFevDBB+Xv76/mzZuratWqOn78uO3+DzzwgBo1aiRfX19NmDBBx44d044dO/Ttt98qNzdXY8eOla+vr2rUqKF//etfWrp0qe1nvb29dccdd8jHx0d+fn52OeLj47VlyxY9++yzqlKlivz8/PTkk08qPz9f33333RWfxxdffKGmTZtqwIAB8vLyUrNmzfTBBx+oadOm+uijj3TzzTerV69e8vLyUtOmTTVs2DAtW7bM9vPt27dXmzZt5OHhofbt26tChQoaMmSIvLy81LVrV+Xm5ioxMdF2/4kTJyo0NFRVqlTRmDFjtG7dOuXm5mrVqlUaNGiQOnToIE9PT3Xo0EGDBg2y21fPnj3VpEkT+fj46M4771RaWpqSkpK0c+dO7dq1S5MnT1ZgYKBCQkI0fvx4ffHFF0pJSbH9/KhRo+Tn56fw8HC1a9dOhw8fvuTvxNfXV99//73WrVsnDw8Pffrpp7r//vuv+LsEgPLEy+wAAADn8/Hx0WuvvaY777xTixcvVkhIiEM/HxwcLIvFIkny8Cj8/20VK1a03e7h4WGbxiZJtWvXtn3t7++v4OBgJSQk6Pjx40pOTlbbtm1ttxuGoby8PCUlJUmSqlSpYtvHnxUVkjp16ti2eXp6qkaNGnal7XJOnz6tmjVr2m1r3LixJCkpKUnXXXed3W21a9e2e9zg4GC7/V74Oyj6/Vz4ewgPD7d9XaNGDeXm5urs2bNKTEy0ew5F+/r6669t31epUsX2tZeXl+2xjx07poKCAnXu3Nnu5318fBQfH3/Jn/f29r5sWX7ttdf0xhtv6NVXX9XYsWPVqlUrPf/882rYsOEl7w8A5RGlCQDKibp162rKlCl66qmn1K9fP9t2T09PSbJbHODCIxbSH4WgpE6fPm37Oj09XSkpKapVq5by8/NVt25drVu3zu72pKQkhYaGXnFftWrVklR4XlTRh/qCggKdOHHCriRcTo0aNS46IrVq1SqFhYWpVq1aiouLs7stPj7e7nEd/T0kJCSoXr16kgrPc/L391dISIhq1659xX1dTvXq1eXn56fNmzfbXrvc3FzFx8crPDxcW7duLXE+q9Wq3bt369FHH9XTTz+tkydPasaMGZowYYJWrVrlwDMFgLKN6XkAUI706tVL/fv31/Lly23bwsLCVKlSJX3xxRcyDEO7du2yKzV/xeLFi3Xo0CFlZWVp2rRpuu6669SsWTN17dpVGRkZeuutt5Sbm6tz585p/Pjxevzxx0tUSKpWrarOnTtr6tSpOnPmjLKzs/Xyyy+roKCgRCvl3Xbbbdq9e7c++eQTFRQUaOfOnZo5c6a8vLzUv39/ff3111q7dq0KCgq0e/duLVq0SP379//Lv4fZs2crPT1dCQkJmjNnju644w55e3trwIABWr58uX7++WcVFBRo06ZNWr58eYn21aJFC4WHh2vmzJnKyMhQdna2pk+frqFDh6qgoOCKP+/r6yupcBU9Dw8PTZ06Va+99ppycnIUGhoqX19fh49EAkBZR2kCgHLm6aeftpuG5uPjoylTpmjt2rVq1aqVZs6cqbvuuutv7aNbt24aMWKEbrrpJqWmpmrevHny8PBQYGCglixZos2bN+umm25St27d5OHhoTfffLPEj/3SSy+pTp06uvPOO3XDDTdo3759+u9//2s3de5y6tatq4ULF2rp0qWKiorS2LFjNWHCBHXq1EktW7bU66+/rkWLFqlNmzYaPXq07rnnHo0YMeIv/x7q1q2r3r176/bbb1dkZKSefvppSdKtt96qiRMnaurUqWrTpo2ef/55PfXUU+rbt+8VH9PLy0sLFixQYmKievTooU6dOikuLk7vvPOOrRAVJyIiQq1bt9aNN96o7777Tq+99poOHjyoTp066YYbblBaWpqmTJnyl58zAJRFFuOvnBEMAAAu69ixY7r55pu1ceNGu/O7AADuiSNNAAAAAFAMShMAAAAAFIPpeQAAAABQDI40AQAAAEAxKE0AAAAAUAxKEwAAAAAUg9IEAAAAAMWgNAEAAABAMShNAAAAAFAMShMAAAAAFIPSBAAAAADFoDQBAAAAQDH+H0p3M7hS+actAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets see the variability explained as function of the num_of_components\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.plot(num_components,var_explained)\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel('Percent. of varuability explained')\n",
    "plt.title('% explained by num of components')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:34.047861Z",
     "start_time": "2023-10-07T12:10:33.685695Z"
    }
   },
   "outputs": [],
   "source": [
    "# We can see from the above graph that we cannot really choose the value of percentage of variability explained as it is \n",
    "# ever increasing. We will look at cv score now.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:34.048041Z",
     "start_time": "2023-10-07T12:10:33.689456Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "plt.plot(num_components,scores,marker = 'o')\n",
    "plt.xlabel('Num of components')\n",
    "plt.ylabel('CV score - 10 fold')\n",
    "plt.title('Cross Validation')\n",
    "plt.xticks = num_components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:34.048099Z",
     "start_time": "2023-10-07T12:10:33.817465Z"
    }
   },
   "outputs": [],
   "source": [
    "# that;s interesting, although the graph looks very distorted, the y has values only between 11600 to 128000\n",
    "# The best error is for num_components = 18"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.7.2 Partial Least Squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:34.048151Z",
     "start_time": "2023-10-07T12:10:33.823654Z"
    }
   },
   "outputs": [],
   "source": [
    "def pls_regression(X,y):\n",
    "    num_components = X.shape[1]\n",
    "    scores = []\n",
    "    \n",
    "    for n_component in range(1,num_components+1):\n",
    "        scores.append(-np.mean(cross_val_score(PLSRegression(n_components=n_component,scale=True)\n",
    "                                               ,X_pca,y,cv = 10,scoring = 'neg_mean_squared_error')))\n",
    "        \n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:34.315003Z",
     "start_time": "2023-10-07T12:10:33.828783Z"
    }
   },
   "outputs": [],
   "source": [
    "pls_scores = pls_regression(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:34.331081Z",
     "start_time": "2023-10-07T12:10:34.170521Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'PARTIAL LEAST SQUARES')"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x800 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "plt.plot(num_components,pls_scores,marker = 'o')\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel('CV score - 10 fold')\n",
    "plt.title('PARTIAL LEAST SQUARES')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:34.331230Z",
     "start_time": "2023-10-07T12:10:34.294568Z"
    }
   },
   "outputs": [],
   "source": [
    "# we can see here that the best result is when num_component = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:10:34.331306Z",
     "start_time": "2023-10-07T12:10:34.301020Z"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
